code: def _position_features(
      graph, key, feature_dim=4, scale=1, wavelength=10000
  ):
      # pos = torch.tensor(graph[key]).float()
      if isinstance(graph[key], torch.Tensor):
          pos = graph[key].float()
      else:
          pos = torch.tensor(graph[key]).float()
      assert pos.ndim <= 2
      if pos.ndim == 1:
          pos = pos.unsqueeze(-1)
      batch_size, n_feats = pos.shape
      pos = pos.view(-1)
      pos *= scale
  
      cycle_range = torch.arange(0, feature_dim // 2).float() / (
          feature_dim // 2
      )
      sins = torch.sin(
          pos.unsqueeze(-1) / wavelength ** cycle_range.unsqueeze(0)
      )
      coss = torch.cos(
          pos.unsqueeze(-1) / wavelength ** cycle_range.unsqueeze(0)
      )
      # dim-by-2
      m = torch.cat((coss, sins), dim=-1)
      graph[key] = m.view(batch_size, -1)
  
imports: import logging
  import networkx
  import numpy
  import torch
  import torch.nn
  from deepsnap.graph import Graph
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.contrib.transform.identity import compute_identity
label_desc: Use image node_id 6 for calling the FeatureAugment obj's underlying member method code with example usage: obj._position_features(graph, key, feature_dim, scale, wavelength) without return types